The present application relates generally to systems and methods of analyzing and exploring data, and more specifically to an improved system and method of analyzing, exploring, and comprehending information relationships in data.
In recent years, computer systems have enabled individuals and organizations to capture and store vast quantities of data. The existence of such large quantities of data has lead to an ever increasing need for improved systems and methods of analyzing and exploring data. For example, spreadsheets have traditionally been employed as a tool for interacting with and analyzing data. Spreadsheets typically organize data in rows and columns. When implemented as a computer program running on a computer system, spreadsheets are typically operative to manipulate the rows and columns of data, to apply algebraic operations to the data, and to explore various “what if” scenarios based on the data analysis requirements of a user. Further, spreadsheet computer programs typically allow the user to generate graphical representations of the data for subsequent display on a video monitor.
Traditional spreadsheets and spreadsheet computer programs have drawbacks, however, in that they are limited in the complexity of the data that can be manipulated. For example, spreadsheet computer programs typically operate on data stored in flat files, and are generally unable to handle complex data sets involving multiple data dimensions and/or multi-variable data presentations. The graphical data representations that can be generated by traditional spreadsheet computer programs also suffer significant limitations.
More recently, a number of computerized visualization techniques have been developed for analyzing and displaying data acquired by computer systems. For example, geographic information system (GIS) technology can be employed on a computer system to capture, store, analyze, and display visual representations of geographically referenced data, i.e., data that is identified according to its geographic location. In a typical geographic information system, data can be captured and plotted on a map to relate various groups of data (e.g., the levels of rainfall occurring at fixed geographic locations, and the locations of marshes) in a spatial context, thereby allowing a user to draw conclusions about the data (e.g., which marshes are likely to dry up) and to extract new information from the data relationships (e.g., information relating to how humans might best interact with the various marsh locations to protect fragile ecosystems). A typical GIS can organize different groups of data in respective map layers or overlays. Geographic information systems are also typically capable of rendering symbols used on maps (e.g., the symbols used to represent the data corresponding to the rainfall levels and the marshes) based on information relating to the data group that the symbols represent. For example, a GIS can vary the size, the shape, and/or the color/shade of particular symbols based on the rainfall levels in particular locations and/or the likelihood of the marshes in those locations to dry up.
Geographic information systems also have drawbacks, however, in that they are typically unsuited for use as a general data analysis tool. For example, as explained above, a conventional GIS is designed to handle data identified according to its fixed geographic location. For this reason, data that does not have corresponding geographic references normally cannot be analyzed by a GIS. Even if representative location references were derived for a set of data before providing the data to a GIS for analysis, the mapping of the data by the GIS would likely be constrained by the fixed nature of the data locations, thereby limiting the number of possible visual data representations that can be presented to a user, and in turn, limiting the user's ability to comprehend new information from the data relationships.
It would therefore be desirable to have an improved system and method of analyzing, exploring, and comprehending information relationships within various groups of data. Such an improved data exploration system would provide multiple visual data representations that are adaptable to the needs of a system user, while avoiding the drawbacks of conventional data analysis systems and techniques.